Vitali Liauchuk

TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.

TL;DR: The achieved results are promising given the difficulty of the tasks and weakly‐labeled nature of the ground truth, however, further research is needed to improve the practical utility of image analysis methods for this task.

TL;DR: An overview of the ImageCLEF 2017 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2017, shows the interest in this benchmarking campaign despite the fact that all four tasks were new and had to create their own community.

TL;DR: An overview of the ImageCLEF 2018 evaluation campaign is presented, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign.

TL;DR: An overview of the ImageCLEF 2019 lab is presented, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019, and shows an important interest in this benchmark campaign.